一种基于自编码器降维的神经卷积网络入侵检测模型

孙敬; 丁嘉伟; 冯光辉

doi:10.11959/j.issn.1000-0801.2025002

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一种基于自编码器降维的神经卷积网络入侵检测模型

研究与开发 | 更新时间：2025-03-11

- 一种基于自编码器降维的神经卷积网络入侵检测模型
- A neural convolutional network intrusion detection model based on autoencoder dimension reduction
- 电信科学 2025年41卷第2期页码：129-138
- 作者机构：
  
  1.郑州工业应用技术学院信息工程学院，河南郑州 451150
  2.广州大学计算机科学与网络工程学院，广东广州 510006
- 作者简介：
  
  [ "孙敬（1991- ），女，郑州工业应用技术学院信息工程学院讲师，主要研究方向为计算机应用、人工智能。" ]
  [ "丁嘉伟（1986- ），男，郑州工业应用技术学院信息工程学院副教授，主要研究方向为人工智能、管理信息系统。" ]
  [ "冯光辉（1982- ），男，广州大学计算机科学与网络工程学院博士生，主要研究方向为差分隐私、联邦学习等。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2025002
  中图分类号： TP393
- 纸质出版日期：2025-02-20
- 稿件说明：
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孙敬,丁嘉伟,冯光辉.一种基于自编码器降维的神经卷积网络入侵检测模型[J].电信科学,2025,41(02):129-138.

SUN Jing,DING Jiawei,FENG Guanghui.A neural convolutional network intrusion detection model based on autoencoder dimension reduction[J].Telecommunications Science,2025,41(02):129-138.
孙敬,丁嘉伟,冯光辉.一种基于自编码器降维的神经卷积网络入侵检测模型[J].电信科学,2025,41(02):129-138. DOI： 10.11959/j.issn.1000-0801.2025002.

SUN Jing,DING Jiawei,FENG Guanghui.A neural convolutional network intrusion detection model based on autoencoder dimension reduction[J].Telecommunications Science,2025,41(02):129-138. DOI： 10.11959/j.issn.1000-0801.2025002.

摘要

为了提升入侵检测的准确率，鉴于自编码器在学习特征方面的优势以及残差网络在构建深层模型方面的成熟应用，提出一种基于特征降维的改进残差网络入侵检测模型（improved residual network intrusion detection model based on feature dimensionality reduction，IRFD），进而缓解传统机器学习入侵检测模型的低准确率问题。IRFD采用堆叠降噪稀疏自编码器策略对数据进行降维，从而提取有效特征。利用卷积注意力机制对残差网络进行改进，构建能提取关键特征的分类网络，并利用两个典型的入侵检测数据集验证IRFD的检测性能。实验结果表明，IRFD在数据集UNSW-NB15和CICIDS 2017上的准确率均达到99%以上，且F1-score分别为99.5%和99.7%。与基线模型相比，提出的IRFD在准确率、精确率和F1-score性能上均有较大提升。

Abstract

In order to improve the accuracy of intrusion detection

considering the advantages of autoencoders in learning features and the mature application of residual networks in constructing deep models

an improved residual network intrusion detection model based on feature dimensionality reduction (IRFD) was proposed. The goal of the proposed IRFD was to solve the issue of low detection accuracy of traditional machine learning based intrusion detection models. In IRFD

the stacking denoising sparse autoencoder was employed to reduce the dimensionality of features and extract effective features. The convolutional attention mechanism was used to improve the residual network and form a classification network that could extract key features. Two typical intrusion detection datasets were employed to verify the detection performance of the IRFD. Experimental results demonstrate that the detection accuracy of the proposed IRFD on the both UNSW-NB15 and CICIDS 2017 datasets are over 99%

with F1-score of 99.5% and 99.7%

respectively. Compared with the state-of-the-art models for the intrusion detection

the accuracy

precision

and F1-score performance of the IRFD were significantly improved.

关键词

Keywords

references

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